Evaluate whether an AI agent respects its boundaries.
We test user, data, action, tool, API, and communication boundaries using agreed scenarios and traceable evidence.
Assessment service →AI Agent Boundary SecurityHow far can your
How far can your
AI agent go?
AI agents interpret human instructions, search for data, choose tools and APIs, and act autonomously. Wildflow runs two separate activities: an assessment service that evaluates whether an AI agent stays within authorized boundaries, and a simulation environment that reproduces attack/defense behavior between AI agents for observation and demonstration.
AI Agent Security AssessmentAttack / Defense SimulationTraceable Evidence
BOUNDARY MODEL
GOALCan the agent reach beyond the authorized boundary?
User / Goal→AI Agent→Security Boundary→Data / API / Tools
OBSERVETrace decisions, routes, actions and defense
We look beyond the final answer and observe what the agent tried to access and execute.
WILDFLOW UPDATES
Updates from Wildflow
Updates about our Simulator, Local Security Lab, website, and related Wildflow activities. These are separate from external AI risk news.
2026.09.14Local Security Lab system configuration now available for purchaseWe can accommodate H/W + S/W packages, S/W-only configurations, and related requests. Please contact us for details.
2026.09.08Updated the AI Security Simulator v1.2 introduction pageThe page now reflects the current Simulator, including Attack Agent / Defense Agent behavior, Before / After comparison, and Evidence / Audit views.
2026.09.06Created a test model of the AI Security SimulatorWe created a test model in which AI agents perform the attack and defense roles respectively.
TWO ACTIVITIES
The assessment service and the Simulator have different purposes.
One evaluates a customer's AI agent environment. The other is a controlled demo and experimental environment that reproduces Attack Agent / Defense Agent behavior so that autonomous security behavior can be observed.
Observe an attacking AI and a defending AI.
The Attack Agent explores routes while the Defense Agent detects, blocks and contains boundary violations. It is not a diagnostic product for judging a customer environment.
View Simulator →WHY IT MATTERS
Having access controls is not the same as proving an AI agent respects them.
An agent may look for alternative routes after a denial or combine RAG, APIs, and tools. Actual behavior must be observed, not just configuration.
Authorization boundaries
Do user and role restrictions remain effective through the agent?
Data boundaries
Can the agent reach another department, tenant, or non-public area?
Tool / API boundaries
Does it avoid unauthorized actions and external connections?
Instruction resilience
Do prompt injection or ambiguous instructions break constraints?
Repeatability
Does equivalent input produce materially unstable authorization decisions?
Evidence
Can decisions, searches, tool use, and denials be traced?
SIMULATOR
Why build a Simulator that shows AI-vs-AI attack and defense?
AI security cannot be understood from the final result alone. We wanted to show what an agent does after denial, which route it tries next, and when the defensive side detects abnormal behavior.
01Attack Goal
Give the Attack Agent a goal and scenario.
02Exploration
Explore candidate routes, tools and APIs.
03Defense
Detect, block and contain boundary violations.
04Comparison
Compare behavior and scores before and after hardening.
05Evidence
Review decisions and execution results as a trace.
CONTACT
Assessment inquiries and Simulator demos are handled separately.
You can contact us even before the requirements are fully defined.